Temporal Bayesian classifiers for modelling muscular dystrophy expression data

The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. We show that this classifier improves the classification of microarray data and at the same time ensures that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy

The robust selection of predictive genes via a simple classifier

Identifying genes that direct the mechanism of a disease from expression data is extremely useful in understanding how that mechanism works. This in turn may lead to better diagnoses and potentially can lead to a cure for that disease. This task becomes extremely challenging when the data are characterised by only a small number of samples and a high number of dimensions, as it is often the case with gene expression data. Motivated by this challenge, we present a general framework that focuses on simplicity and data perturbation. These are the keys for the robust identification of the most predictive features in such data. Within this framework, we propose a simple selective na¨ıve Bayes classifier discovered using a global search technique, and combine it with data perturbation to increase its robustness to small sample sizes. An extensive validation of the method was carried out using two applied datasets from the field of microarrays and a simulated dataset, all confounded by small sample sizes and high dimensionality. The method has been shown capable of identifying genes previously confirmed or associated with prostate cancer and viral infections

From Golden Spirals to Constant Slope Surfaces

In this paper, we find all constant slope surfaces in the Euclidean 3-space, namely those surfaces for which the position vector of a point of the surface makes constant angle with the normal at the surface in that point. These surfaces could be thought as the bi-dimensional analogue of the generalized helices. Some pictures are drawn by using the parametric equations we found.Comment: 11 pages, 8 figure

A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Voids in the LCRS versus CDM Models

We have analyzed the distribution of void sizes in the two-dimensional slices of the Las Campanas Redshift Survey (LCRS). Fourteen volume-limited subsamples were extracted from the six slices to cover a large part of the survey and to test the robustness of the results against cosmic variance. Thirteen samples were randomly culled to produce homogeneously selected samples. We then studied the relationship between the cumulative area covered by voids and the void size as a property of the void hierarchy. We find that the distribution of void sizes scales with the mean galaxy separation, $\lambda$. In particular, we find that the size of voids covering half of the area is given by D_{med} \approx \lambda + (12\pm3) \h^{-2}Mpc. Next, by employing an environmental density threshold criterion to identify mock galaxies, we were able to extend this analysis to mock samples from dynamical $n$-body simulations of Cold Dark Matter (CDM) models. To reproduce the observed void statistics, overdensity thresholds of $\delta_{th} \approx 0 ... 1$ are necessary. We have compared standard (SCDM), open (OCDM), vacuum energy dominated ($\Lambda$CDM), and broken scale invariant CDM models (BCDM): we find that both the void coverage distribution and the two-point correlation function provide important and complementary information on the large-scale matter distribution. The dependence of the void statistics on the threshold criterion for the mock galaxy indentification shows that the galaxy biasing is more crucial for the void size distribution than are differences between the cosmological models.Comment: 10 pages, 8 eps figures, submitted to MNRA

Natural Resource Information System, remote sensing studies

Potential applications of remote sensing data were reviewed, and available imagery was interpreted to provide input to a demonstration data base. A literature review was conducted to determine the types and qualities of imagery required to satisfy identified data needs. Ektachrome imagery available over the demonstration areas was reviewed to establish the feasibility of interpreting cultural features, range condition, and timber type. Using the same imagery, a land use map was prepared for the demonstration area. The feasibility of identifying commercial timber areas using a density slicing technique was tested on multispectral imagery available for a portion of the demonstration area